code: def test_recognition_zoo(arch_name):
      batch_size = 2
      # Model
      predictor = recognition.zoo.recognition_predictor(
          arch_name, pretrained=False
      )
      predictor.model.eval()
      # object check
      assert isinstance(predictor, RecognitionPredictor)
      input_tensor = torch.rand((batch_size, 3, 128, 128))
      if torch.cuda.is_available():
          predictor.model.cuda()
          input_tensor = input_tensor.cuda()
  
      with torch.no_grad():
          out = predictor(input_tensor)
      out = predictor(input_tensor)
      assert isinstance(out, list) and len(out) == batch_size
      assert all(
          isinstance(word, str) and isinstance(conf, float)
          for word, conf in out
      )
  
file_path: doctr/tests/pytorch/test_models_recognition_pt.py
imports: import os
  import tempfile
  import onnxruntime
  import psutil
  import pytest
  import torch
  from doctr.models import recognition
  from doctr.models.recognition.crnn.pytorch import CTCPostProcessor
  from doctr.models.recognition.master.pytorch import MASTERPostProcessor
  from doctr.models.recognition.parseq.pytorch import PARSeqPostProcessor
  from doctr.models.recognition.predictor import RecognitionPredictor
  from doctr.models.recognition.sar.pytorch import SARPostProcessor
  from doctr.models.recognition.vitstr.pytorch import ViTSTRPostProcessor
  from doctr.models.utils import export_model_to_onnx
label_desc: Use image node_id 3 for calling a global function with example usage: test_recognition_zoo(arch_name) without return types